class_docstr: Input is the dy and output is the dx.
  Use the function.backward in the forward_impl.
  Use the function.forward in the backward_impl.
class_docstr_tok: ["Input","is","the","dy","and","output","is","the","dx",".","Use","the","function.backward","in","the","forward_impl",".","Use","the","function.forward","in","the","backward_impl","."]
code: def _create_fwd_inputs_outputs(self, inputs, outputs):
      dx = inputs[0].data
      ishape = self.yshape
      oshape = dx.shape
      inputs_fwd = [
          nn.Variable(ishape, need_grad=True),
          nn.Variable(oshape, need_grad=False),
      ]
      outputs_fwd = [nn.Variable(oshape)]
      # inputs_fwd[0]:  dy
      # inputs_fwd[1]:  x0
      # outputs_fwd[0]: dx
      return inputs_fwd, outputs_fwd
  
file_path: nnabla/python/src/nnabla/backward_function/max_pooling_backward.py
imports: import nnabla
  import nnabla.function
  from nnabla.function import PythonFunction
label_desc: Use image node_id 4 for calling the MaxPoolingBackwardDataGrad obj's underlying member method code with example usage: obj._create_fwd_inputs_outputs(inputs, outputs) and returns: inputs_fwd,  outputs_fwd